Alfalfa is an example of a crop for which an alfalfa producer attempts to optimize crop quality by harvesting it at an optimum time. Chemical and nutritional characteristics of alfalfa are not constant over its growing life. As an alfalfa crop matures, fiber content increases while protein content and digestibility decrease. The optimum chemical composition of an alfalfa forage is dependent on its intended use. In the case of alfalfa forage for high-producing dairy cattle, it has been determined that an optimum alfalfa forage has a neutral detergent fiber (NDF) content of 40 percent of dry matter (DM). Although crude protein (CP) content, digestibility and yield are important characteristics of an alfalfa forage crop, NDF content is generally the main characteristic upon which a harvest time decision is based. This is so because the NDF content is most directly determinative of the amount of feed supplements that will be required, and also because at present CP and digestibility have been found to be more difficult to predict accurately.
Harvesting alfalfa at the wrong time is costly to the producer. When alfalfa is the only forage fed, each unit of alfalfa forage NDF content over 40% DM has been estimated to amount to an increased cost for the dairy cattle operation of $33 per cow, per year. The increased cost arises, in part, because more grain is needed in the diet to give the animals sufficient energy and protein, and to reduce the filling effect of NDF in the diet. Also, the reduced digestibility of the NDF as the alfalfa matures increases the filling effect of the forage, reducing DM and energy intake, which decreases milk production. Alfalfa forage with an NDF content below 40% DM is undesirable also. First, the immature alfalfa forage crop has a lower yield. Also, the cows will need to eat more of the immature alfalfa to obtain the needed fiber, reducing the proportion of grain in the diet. Because grain has a higher energy content than alfalfa, this will reduce the energy content of the diet and will thus reduce milk production. Even further, the cows will get excess protein when fed the immature, high-protein alfalfa forage. Excess protein is undesirable because it is wasteful, may lead to additional environmental contamination, costs the animal additional energy to metabolize and excrete, and may reduce reproductive performance.
Determining the right harvest time has proven to be difficult. First, visual examination of a standing alfalfa crop has proven to be an inaccurate method. Second, a "scissors-cut" method involving lab testing of the cut samples is not a viable option for several reasons. The major problem is that fresh-cut forage is unstable due to respiratory enzymes; as sugars respire NDF content increases significantly. There is not enough time prior to the time that the alfalfa crop needs to be harvested to make this an accurate prediction method. From one day to the next, NDF content will increase on the order of 0.4% to 0.8% DM, so the window of harvesting is very short. Further, lab testing--even testing with the recent technology of near-infrared spectroscopy (NIRS)--requires work on the part of the producer to cut and prepare samples for testing. The testing also requires transportation time and costs in sending the samples to a laboratory.
Prediction modeling has become widely recognized as a desirable way of predicting, prior to harvest, various crop characteristics of an alfalfa crop. Various morphological and climatological variables and their relationship to various alfalfa crop characteristics have been studied. Using regression analyses, crop characteristic prediction equations have been developed and are continuously evolving as more empirical data upon which to base the equations are collected.
For example, in the 1980s Fick and Onstad developed a series of equations for various alfalfa crop characteristics including NDF content. Fick & Onstad, "Statistical Models for Predicting Alfalfa Herbage Quality from Morphological or Weather Data," J. Prod. Agric., Vol. 1, No. 2, 1988 ("Fick & Onstad, 1988"). The equations were based on a sample set including both first and subsequent cuttings in the same year. The equations were based on a single one of the following morphological and climatological variables: age of the herbage in days; growing degree days (GDDs) with a 41.degree. F. (5.degree. C.) base temperature and accumulated from the start of growth or regrowth; mean morphological stage by weight (MSW); leaf proportion of the herbage (LP); latitude of the collection site; and hours of light received during growth, again accumulated from the start of growth or regrowth. Fick and Onstad developed the following equation for total NDF content of alfalfa based on their data set: EQU NDF (total plant)=20.5+0.0335 (GDD)
(Id., Table 6.) Although Fick and Onstad studied the relationship between GDD.sup.2 and total NDF content, GDD.sup.2 was not found to be significant, and hence the relationship between total NDF content and GDD was found to be linear for the data set used in the study. In addition, Fick and Onstad found that the equations they developed based on morphological properties were better predictors, compared to the equations they developed based on climatological variables.
Sanderson later studied the Fick and Onstad single-variable equations and found that the equations were most valid for the region in which they were developed, and that calibration for specific geographic regions and frequent recalibration were necessary. Sanderson, "Crop Quality & Utilization," Crop Sci. 32:245-250, 1992. Also, a recent study by Cherney similarly concluded that it would be very difficult to produce a simple and fast method for predicting NDF that will hold up under a range of environments, and that prediction equations must be developed locally to offer any hope for reasonably accurate predictions. Cherney, "Spring Alfalfa Harvest in Relation to Growing Degree Days," Proceedings for the 25th National Alfalfa Symposium held Feb. 27-28, 1995.
Fick and Onstad also used step-wise multiple regression analysis to develop crop characteristic equations based on more than single factors. Fick & Onstad, 1988. They found that the best fitting equations included only two independent variables, one of which was always LP. For NDF, the equation was: EQU NDF=39.6+11.5 ln (MSW+1.0)-33.0 LP
(Id., Table 7.) For this equation, Fick and Onstad found the r.sup.2 value to be equal to 0.83 and the root mean square error (RMSE) to be 2.79. The r.sup.2 measure is the fraction of total variation explained by the equation. In other words, with r.sup.2 equal to 0.83, the equation explains 83% of all variation. The RMSE measure is a measure of deviation in the prediction equation. Sixty-eight percent of all observations fall with the range of plus-or-minus one RMSE, which in this case is .+-.2.79% DM, and 90% fall within the range of plus-or-minus two RMSEs. Thus, equations with a higher r.sup.2 measure and a lower RMSE measure are better fits for the data set. Finding values for both MSW and LP are laborious processes requiring special equipment. In particular, the processes involve sorting samples, drying the samples using an oven, and weighing the samples with a scale. As such, an equation utilizing variables of MSW and LP are not practicable for use by the producer.
Building on the study by Fick and Onstad, another study focusing on morphological factors as predictors of an alfalfa forage characteristics was done by Hintz and Albrecht. Hintz and Albrecht developed equations based on multiple variables using step-wise multiple regression. The prediction method using the equations of Hintz and Albrecht has been referred to as the "predictive equations for alfalfa quality" (PEAQ) method. The PEAQ equation for NDF is as follows: EQU NDF=16.89+0.27 (MAXHT)+0.81 (MAX)
where MAXHT is equal to the height of the tallest stem present in each sample in cm, and MAX is equal to the morphological stage of development of the most mature stem present in each sample. See Hintz and Albrecht, "Prediction of Alfalfa Chemical Composition from Maturity and Plant Morphology," Crop Sci. 31:1561-1565 (1991); and also Owens, Albrecht & Hintz, "A Rapid Method for Predicting Alfalfa Quality in the Field," J. Prod. Agric. 8:491-495 (1995). (The latter reference provides in its Table 1 the values for the variable "MAX" used for various morphological stages.)
Various individuals, including extension agents, have begun to make use of the various alfalfa crop characteristic prediction equations by collecting crop and/or field information needed for the equation, and then plugging that information into an equation to come up with a prediction. This has been done using a hand-held calculator or a personal computer that has the prediction equation programmed in to it.
Prediction models may create additional work for the producer that the producer may not have time to perform at the busy time of harvesting. Equations based on morphological factors, for example, require field sampling that the producer would not otherwise perform. Also, with equations based on climatological variables, the producer needs to obtain the climatological data. This may be easy for some prediction models using standard climatological variables. For other prediction models, however, the climatological data in the form required for entry into the prediction equation may not be readily available to the producer. In addition, the continued evolution of prediction equations will likely lead to the development of prediction models that are more accurate predictors but which are based upon climatological variables not available from any source and which may only be derived after complicated calculations of available climatological data.